Our course finder pages contain all the most up-to-date information about the Financial Computing MSc, including details of the programme structure, compulsory and elective modules and study options.
Below is a full list of all modules which are expected to be available to students on this programme across the semesters. Please note that this is for information only and may be subject to change. Click the link above for accurate information about which of these modules are compulsory and elective for each semester of your MSc programme.
Modules with codes beginning MTH are taught by the School of Mathematical Sciences (SMS), providing a solid understanding of the principles of mathematical finance. Modules with codes beginning ECS are taught by the School of Electronic Engineering and Computer Science (EECS), and focus on key aspects of technological implementation. Modules are assessed by a mixture of in-term assessment and final examinations, with examinations being held in January and May.
This module covers the advanced programming techniques in C++ that are widely used by professional software engineers and quantitative analysts & developers. The most important of these techniques is object-oriented programming, embracing the concepts of encapsulation, inheritance and polymorphism. We then use these techniques to price a wide range of financial derivatives numerically, using several different pricing models and numerical methods. On completion of this module, you will have acquired the key skills needed to apply for your first role as a junior ‘quant’ or software developer in a financial institution.
This module builds on the earlier module "Machine Learning with Python", covering a number of advanced techniques in machine learning, such as dimensionality reduction, support vector machines, decision trees, random forests, and clustering. Although the underlying theoretical ideas are clearly explained, this module is very hands-on, and you will implement various applications using Python in the weekly coursework assignments.
The module will introduce concepts associated with advanced object-oriented programming concepts, such as inheritance and polymorphism, creating templates, advanced working with exception handling, stream input/output management, associative containers, algorithms, stacks, queues and binary trees, different search and sort methods, namespaces, advanced string class methods, and working with libraries, e.g. boost and STL. It also explores some of the contexts in which these techniques are useful.
This module provides an overview of techniques used in Artificial Intelligence including agent modelling, problem formulation, search, logic, probability and machine learning.
Cloud Computing has transformed how services and applications are delivered. Thanks to the rise of virtualisation technology and new programming paradigms, applications can quickly be delivered to a growing audience, without the need to physically own and configure the infrastructure. The Cloud Computing module will cover the main characteristics of Cloud Computing, including the enabling technologies, main software and service paradigms underpinning it, as well as related aspects, namely security, privacy, ethical concerns
The Internet interconnects billions of machines, ranging from high end servers to limited capacity embedded sensing devices. Distributed systems are built to take advantage of multiple interconnected machines and achieve common goals with them. The module will cover the fundamental concepts and technical challenges of building distributed systems. The topics will include the characteristics of network communications for applications, application-level communication protocols, the concept of synchronization (implications, role of consistency modes and protocols), as well as the impact of data replication, and options for tolerating failures.
The project component of the MSc programme will give you the opportunity to undertake some significant and advanced study in an area of interest, under the guidance of an expert in that field. Many projects involve a substantial amount of programming and analysis. Your project will be assessed by a written dissertation (of up to 60 pages) which you will submit in early September.
Possible project topics may include:
This module will provide students with a general understanding of current applications of data analytics to finance and in particular to derivatives and investment banking. It will introduce a range of analytical tools such as volatility surface management, yield curve evolution and FX volatility/correlation management. It will also provide you with an overview of some standard tools in the field such as Python, R, Excel/VBA and the Power BI Excel functionality. Students are not expected to have any familiarity with coding or any of the topics above, as the module will develop these from scratch. It will provide you with the understanding of a field necessary to prepare for a career in finance in roles such as trading, structuring, management, risk management and quantitative positions in investment banks and hedge funds.
This module first introduces you to various types of financial instruments, such as bonds and equities, and the markets in which they are traded. We then explain in detail what financial derivatives are, and how they can be used for hedging and speculation. We also look at how investors can construct optimal portfolios of assets by balancing risk and return in an appropriate way. This module will give you the practical knowledge that is essential for a career in investment banking or financial markets.
This module introduces you to all of the fundamental concepts needed for your future studies in financial mathematics. After reviewing some key ideas from probability theory, we give an overview of some of the most important financial instruments, including shares, forward contracts and options. We next explain how derivative securities can be priced using the principle of no arbitrage. Various models for pricing options are then considered in detail, including the discrete-time binomial model and the continuous-time Black-Scholes model.
Recent approaches to systems programming frequently involve functional programming either overtly in the sense that they use modern functional programming languages for rapid prototyping, or more covertly in that they use techniques developed in the functional setting as a way of lending greater structure and clarity to code. This module gives a structured introduction to programming in the modern industrial functional language Haskell, and to techniques such as map-reduce and monadic programming.
This module will provide you with the necessary skills and techniques needed to investigate a variety of practical problems in mathematical finance. It is based on C++, the programming language of choice for many practitioners in the finance industry. You will learn about the basic concepts of the procedural part of C++ (inherited from the earlier C language), before being introduced to the fundamental ideas of object-oriented programming. The module is very ‘hands on’, with weekly sessions in the computer laboratory where you can put your theoretical knowledge into practice with a series of interesting and useful assignments.
This module introduces you to some of the key technologies that are widely used for developing software applications in the financial markets and banking sectors. In particular, we focus on three programming environments/languages (Excel, VBA and C++) which are often used in conjunction to build complete trading and risk management systems. It is a highly practical module, focusing on current industry practice, and therefore you will be well equipped to apply for a programming role in a financial institution.